Facial emotion recognition by adaptive processing of tree structures

Wong, Jia-Jun; Cho, Siu-Yeung

doi:10.1145/1141277.1141282

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Table 1, shows that the proposed approach achieves the highest performance in facial expression recognition (89.28%) because we used the properties of Gabor filter and sparse representation in the stage of extract feature and then using the properties of support vector machine (SVM) in classification. Compared with the previously reported work [1,2,24,25,26] in which the experimental settings are similar to ours, In [24], on 7-class facial expression recognition tasks they used Gabor filter to extract features, SVM for classification and reported an accuracy of (85%). Additionally, they also make the same test [25] but using NN classifier and reported an accuracy of (86.42%).…”

Section: Methodsmentioning

confidence: 53%

“…Compared with the previously reported work [1,2,24,25,26] in which the experimental settings are similar to ours, In [24], on 7-class facial expression recognition tasks they used Gabor filter to extract features, SVM for classification and reported an accuracy of (85%). Additionally, they also make the same test [25] but using NN classifier and reported an accuracy of (86.42%).…”

Section: Methodsmentioning

confidence: 53%

“…First approach [1] used sparse representation to extract features and support vector machine (SVM) for classification, the second applies multiple Gabor filter and uses NN as classifier [25] [26], the third approach as done in [2] uses sparse representation classifier after application of Gabor filter. Fourth approach is similar to third one but this time SVM is used as classifier [24], the last is our proposed approach which uses multiple Gabor filters and sparse representation to extract features and then apply SVM as classifier.…”

Section: Methodsmentioning

confidence: 99%

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Robust Facial Expression Recognition via Sparse Representation and Multiple Gabor filters

El-Sayed¹,

Kholy²,

El-Nahas³

2013

IJACSA

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Abstract-Facial expressions recognition plays important role in human communication. It has become one of the most challenging tasks in the pattern recognition field. It has many applications such as: human computer interaction, video surveillance, forensic applications, criminal investigations, and in many other fields. In this paper we propose a method for facial expression recognition (FER). This method provides new insights into two issues in FER: feature extraction and robustness. For feature extraction we are using sparse representation approach after applying multiple Gabor filter and then using support vector machine (SVM) as classifier. We conduct extensive experiments on standard facial expressions database to verify the performance of proposed method. And we compare the result with other approach.

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Section: Methodsmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Robust Facial Expression Recognition via Sparse Representation and Multiple Gabor filters

El-Sayed¹,

Kholy²,

El-Nahas³

2013

IJACSA

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“…Most of these approaches rely on an Arousal-valence model proposed by Jung Hyun Kim or the 2-dimenesional (Stress vs energy) model proposed by Thayer. In Jung Hyun Kim's [7] work, the collected music mood tags and A-V values from 20 subjects were analyzed and the A-V plane was classified into 8 regions, depicting mood by using k-means clustering algorithm. Thayer [19] came up with a dimensional model, plotted along two axes (Stress versus energy), with mood represented by a two-dimensional coordinate system, lying on either of the two axes or the four quadrants formed by the two-dimensional plot.…”

Section: Literature Surveymentioning

confidence: 99%

An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions

Dureha¹

2014

IJCA

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Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This paper presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed in this paper aspires to reduce the overall computational time and the cost of the designed system. It also aims at increasing the accuracy of the designed system. The facial expression recognition module of the proposed algorithm is validated by testing the system against user dependent and user independent dataset. Experimental results indicate that the user dependent results give 100% accuracy, while user independent results for joy and surprise are 100 %, but for sad, anger and fear are 84.3 %, 80 % and is 66% respectively. The overall accuracy of the emotion recognition algorithm, for user independent dataset is 86%. In audio, 100 % recognition rates are obtained for sad, sad-anger and joy-anger but for joy and anger, recognition rates obtained are 95.4% and 90 % respectively. The overall accuracy of the audio emotion recognition algorithm is 98%. Implementation and testing of the proposed algorithm is carried out using an inbuilt camera. Hence, the proposed algorithm reduces the overall cost of the system successfully. Also, on average, the proposed algorithm takes 1.10 sec to generate a playlist based on facial expression. Thus, it yields better performance, in terms of computational time, as compared to the algorithms in the existing literature.

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